new

app.py

@@ -1,16 +1,143 @@
-
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoProcessor, AutoModelForCausalLM
+import spaces
+
+import requests
+import copy
+
+from PIL import Image, ImageDraw, ImageFont 
+import io
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+import random
+import numpy as np
+
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+models = {
+    'microsoft/Florence-2-large-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True).to("cuda").eval(),
+    'microsoft/Florence-2-large': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to("cuda").eval(),
+    'microsoft/Florence-2-base-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True).to("cuda").eval(),
+    'microsoft/Florence-2-base': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to("cuda").eval(),
+}
+
+processors = {
+    'microsoft/Florence-2-large-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True),
+    'microsoft/Florence-2-large': AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True),
+    'microsoft/Florence-2-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True),
+    'microsoft/Florence-2-base': AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True),
+}
+
+
+DESCRIPTION = "# [Florence-2 OCR Demo](https://huggingface.co/microsoft/Florence-2-large)"
+
+colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
+            'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
+
+def fig_to_pil(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf, format='png')
+    buf.seek(0)
+    return Image.open(buf)
+
+@spaces.GPU
+def run_example(task_prompt, image, text_input=None, model_id='microsoft/Florence-2-large'):
+    model = models[model_id]
+    processor = processors[model_id]
+    if text_input is None:
+        prompt = task_prompt
+    else:
+        prompt = task_prompt + text_input
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        early_stopping=False,
+        do_sample=False,
+        num_beams=3,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(
+        generated_text,
+        task=task_prompt,
+        image_size=(image.width, image.height)
+    )
+    return parsed_answer
+
+
+
+
+def process_image(image, task_prompt, text_input=None, model_id='microsoft/Florence-2-large'):
+    image = Image.fromarray(image)  # Convert NumPy array to PIL Image
+   
+    if task_prompt == 'OCR':
+        task_prompt = '<OCR>'
+        results = run_example(task_prompt, image, model_id=model_id)
+        return results, None
+    else:
+        return "", None  # Return empty string and None for unknown task prompts
+
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+
+single_task_list =[
+    'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
+    'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding',
+    'Referring Expression Segmentation', 'Region to Segmentation',
+    'Open Vocabulary Detection', 'Region to Category', 'Region to Description',
+    'OCR', 'OCR with Region'
+]
+
+cascased_task_list =[
+    'Caption + Grounding', 'Detailed Caption + Grounding', 'More Detailed Caption + Grounding'
+]
+
+
+def update_task_dropdown(choice):
+    if choice == 'Cascased task':
+        return gr.Dropdown(choices=cascased_task_list, value='Caption + Grounding')
+    else:
+        return gr.Dropdown(choices=single_task_list, value='Caption')
+
+
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Tab(label="Florence-2 Image Captioning"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value='microsoft/Florence-2-large')
+                task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task')
+                task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption")
+                task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt)
+                text_input = gr.Textbox(label="Text Input (optional)")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+               
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+        gr.Examples(
+            examples=[
+                ["image1.jpg", 'Object Detection'],
+                ["image2.jpg", 'OCR with Region']
+            ],
+            inputs=[input_img, task_prompt],
+            outputs=[output_text],
+            fn=process_image,
+            cache_examples=True,
+            label='Try examples'
+        )
 
-def predict(image):
-    predictions = pipeline(image)
-    return {p["label"]: p["score"] for p in predictions}
+        submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text])
 
-gr.Interface(
-    predict,
-    inputs=gr.Image(label="Upload hot dog candidate", type="filepath"),
-    outputs=gr.Label(num_top_classes=2),
-    title="Hot Dog? Or Not?",
-).launch()
+demo.launch(debug=True)

pre-requirements.txt

@@ -0,0 +1 @@
+pip>=23.0.0

requirements.txt

@@ -1,2 +1,3 @@
-transformers 
-torch
+spaces
+transformers
+timm